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Company, Edited by Foxit PDF Editor Silberschatz−Korth−Sudarshan • Database System Concepts, Fourth Edition. Front Matter. 1 The fundamental concepts and algorithms covered in the book are often based on those used in. by Abraham Silberschatz, Henry F. Korth and S. Sudarshan. instructor's manual which will aid all of the users of our book as much as. Chapter 3: SQL Database System Concepts, 5th Ed. ©Silberschatz, Korth and Sudarshan See maroc-evasion.info for conditions on re-use Chapter 3: SQL „ Data.
Chapter 23, which covers advanced data types and new applications, has material on temporal data, spatial data, multimedia data, and mobile data- bases. This material is an updated version of material that was in Chapter 21 of the third edition.
These sections may be omitted if so desired, without a loss of continuity. It is possible to design courses by using various subsets of the chapters. We outline some of the possibilities here: Alternatively, they could constitute the foundation of an advanced course in object databases. You might choose to use Chapters 15 and 18, while omitting Chapters 16, 17, 19, and 20, if you defer these latter chapters to an advanced course. Model course syllabi, based on the text, can be found on the Web home page of the book see the following section.
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Finally, Sudarshan would like to acknowledge his wife, Sita, for her love and sup- port, two-year old son Madhur for his love, and mother, Indira, for her support. Hank would like to acknowledge his wife, Joan, and his children, Abby and Joe, for their love and understanding. Avi would like to acknowledge his wife Haya, and his son, Aaron, for their patience and support during the revision of this book.
The collection of data, usually referred to as the database, contains information relevant to an enterprise. Database systems are designed to manage large bodies of information. In addition, the database system must ensure the safety of the information stored, despite system crashes or attempts at unauthorized access. If data are to be shared among several users, the system must avoid possible anomalous results.
Because information is so important in most organizations, computer scientists have developed a large body of concepts and techniques for managing data. These concepts and technique form the focus of this book. Here are some representative applications: For customer information, accounts, and loans, and banking transac- tions.
For reservations and schedule information. For student information, course registrations, and grades. For downloads on credit cards and generation of month- ly statements.
For keeping records of calls made, generating monthly bills, maintaining balances on prepaid calling cards, and storing information about the communication networks. For customer, product, and download information. As the list illustrates, databases form an essential part of almost all enterprises today.
Over the course of the last four decades of the twentieth century, use of databases grew in all enterprises. In the early days, very few people interacted directly with database systems, although without realizing it they interacted with databases in- directly — through printed reports such as credit card statements, or through agents such as bank tellers and airline reservation agents.
Then automated teller machines came along and let users interact directly with databases. The internet revolution of the late s sharply increased direct user access to databases. Organizations converted many of their phone interfaces to databases into Web interfaces, and made a variety of services and information available online. For instance, when you access an online bookstore and browse a book or music collec- tion, you are accessing data stored in a database.
When you enter an order online, your order is stored in a database. When you access a Web site, information about you may be retrieved from a database, to select which advertisements should be shown to you. Furthermore, data about your Web accesses may be stored in a database.
New application programs are added to the system as the need arises. For exam- ple, suppose that the savings bank decides to offer checking accounts. Before database management systems DBMSs came along, organizations usu- ally stored information in such systems. This redundancy leads to higher storage and access cost. In addition, it may lead to data inconsis- tency; that is, the various copies of the same data may no longer agree.
Because the designers of the original system did not anticipate this request, there is no application program on hand to meet it. There is, however, an ap- plication program to generate the list of all customers. Both alternatives are obviously unsatisfactory.
As expected, a program to generate such a list does not exist. More responsive data-retrieval systems are required for general use. The data values stored in the database must satisfy cer- tain types of consistency constraints. Developers enforce these constraints in the system by adding appropriate code in the various ap- plication programs. A computer system, like any other mechanical or elec- trical device, is subject to failure.
In many applications, it is crucial that, if a failure occurs, the data be restored to the consistent state that existed prior to the failure. Clearly, it is essential to database consistency that either both the credit and debit occur, or that neither occur. That is, the funds transfer must be atomic — it must happen in its entirety or not at all. For the sake of overall performance of the sys- tem and faster response, many systems allow multiple users to update the data simultaneously.
In such an environment, interaction of concurrent up- dates may result in inconsistent data. Suppose that the pro- grams executing on behalf of each withdrawal read the old balance, reduce that value by the amount being withdrawn, and write the result back. Depending on which one writes the value To guard against this possibility, the system must maintain some form of supervision. Not every user of the database system should be able to access all the data.
For example, in a banking system, payroll personnel need to see only that part of the database that has information about the various bank employees.
They do not need access to information about customer ac- counts. In most of this book, we use a bank enterprise as a running example of a typical data-processing application found in a corporation.
A major purpose of a database system is to provide users with an abstract view of the data. That is, the system hides certain details of how the data are stored and maintained.
The lowest level of abstraction describes how the data are actu- ally stored. The physical level describes complex low-level data structures in detail. The next-higher level of abstraction describes what data are stored in the database, and what relationships exist among those data.
The logical level thus describes the entire database in terms of a small number of relatively simple structures. Although implementation of the simple struc- tures at the logical level may involve complex physical-level structures, the user of the logical level does not need to be aware of this complexity.
Database administrators, who must decide what information to keep in the database, use the logical level of abstraction. The highest level of abstraction describes only part of the entire database.
Even though the logical level uses simpler structures, complexity remains because of the variety of information stored in a large database. Many users of the database system do not need all this information; instead, they need to access only a part of the database. The view level of abstraction exists to simplify their interaction with the system. The system may provide many views for the same database. Figure 1. An analogy to the concept of data types in programming languages may clarify the distinction among levels of abstraction.
Most high-level programming languages support the notion of a record type. For example, in a Pascal-like language, we may declare a record as follows: The language view level view 1 view 2 … view n logical level physical level Figure 1.
Similarly, the database system hides many of the lowest-level storage details from database programmers. Database administrators, on the other hand, may be aware of certain details of the physical organization of the data. Programmers using a programming language work at this level of abstraction. Similarly, database administrators usually work at this level of abstraction.
Finally, at the view level, computer users see a set of application programs that hide details of the data types. In addition to hiding details of the logical level of the database, the views also provide a security mechanism to prevent users from accessing certain parts of the database. For example, tellers in a bank see only that part of the database that has information on customer accounts; they cannot access information about salaries of employees. The collection of information stored in the database at a particular moment is called an instance of the database.
The overall design of the database is called the database schema. Schemas are changed infrequently, if at all. The concept of database schemas and instances can be understood by analogy to a program written in a programming language.
Each variable has a particular value at a given instant. The values of the variables in a program at a point in time correspond to an instance of a database schema. Database systems have several schemas, partitioned according to the levels of ab- straction. The physical schema describes the database design at the physical level, while the logical schema describes the database design at the logical level. A database may also have several schemas at the view level, sometimes called subschemas, that describe different views of the database.
Of these, the logical schema is by far the most important, in terms of its effect on application programs, since programmers construct applications by using the logical schema. The physical schema is hidden beneath the logical schema, and can usually be changed easily without affecting application programs. Application programs are said to exhibit physical data independence if they do not depend on the physical schema, and thus need not be rewritten if the physical schema changes.
We study languages for describing schemas, after introducing the notion of data models in the next section. To illustrate the concept of a data model, we outline two data models in this Both provide a way to describe the design of a database at the logical level. For example, each person is an entity, and bank accounts can be considered as entities. Entities are described in a database by a set of attributes.
For example, the at- tributes account-number and balance may describe one particular account in a bank, and they form attributes of the account entity set. Similarly, attributes customer-name, customer-street address and customer-city may describe a customer entity.
An extra attribute customer-id is used to uniquely identify customers since it may be possible to have two customers with the same name, street address, and city. In the United States, many enterprises use the social-security number of a person a unique number the U. A relationship is an association among several entities. For example, a depositor relationship associates a customer with each account that she has. The set of all enti- ties of the same type and the set of all relationships of the same type are termed an entity set and relationship set, respectively.
The overall logical structure schema of a database can be expressed graphically by an E-R diagram, which is built up from the following components: As an illustration, consider part of a database banking system consisting of customers and of the accounts that these customers have. The E-R diagram indicates that there are two entity sets, customer and account, with attributes as outlined earlier.
The diagram also shows a relationship depositor between customer and account. In addition to entities and relationships, the E-R model represents certain con- straints to which the contents of a database must conform. One important constraint is mapping cardinalities, which express the number of entities to which another en- tity can be associated via a relationship set. For example, if each account must belong to only one customer, the E-R model can express that constraint.
The entity-relationship model is widely used in database design, and Chapter 2 explores it in detail. Each table has multiple columns, and each column has a unique name. One shows details of bank customers, the second shows accounts, and the third shows which accounts belong to which customers. The third table shows which accounts belong to which customers.
For example, account number A belongs to the customer whose customer-id is , namely Johnson, and customers Johnson and Smith share account number A they may share a business venture. The relational model is an example of a record-based model. Each table contains records of a particular type.
The columns of the table correspond to the at- tributes of the record type. For instance, a special character such as a comma may be used to delimit the different attributes of a record, and another special character such as a newline character may be used to delimit records. The relational model hides such low-level implementation details from database developers and users.
The relational data model is the most widely used data model, and a vast majority of current database systems are based on the relational model. Chapters 3 through 7 cover the relational model in detail. The relational model is at a lower level of abstraction than the E-R model. Database designs are often carried out in the E-R model, and then translated to the relational model; Chapter 2 describes the translation process. For example, it is easy to see that the tables customer and account correspond to the entity sets of the same name, while the table depositor corresponds to the relationship set depositor.
We also note that it is possible to create schemas in the relational model that have problems such as unnecessarily duplicated information. For example, suppose we Palo Alto Smith 4 North St.
Rye Hayes 3 Main St. Harrison Turner Putnam Ave. Stamford Jones Main St. Harrison Lindsay Park Ave. Pittsfield Smith 72 North St. Rye a The customer table account-number balance A A A A A A A b The account table customer-id account-number A A A A A A A A c The depositor table Figure 1. Then, to represent the fact that accounts A and A both belong to customer Johnson with customer-id , we would need to store two rows in the customer table.
The values for customer-name, customer-street, and customer-city for Johnson would get unneces- sarily duplicated in the two rows. In Chapter 7, we shall study how to distinguish good schema designs from bad schema designs.
The object-oriented model can be seen as extending the E-R model with notions Chapter 8 examines the object-oriented data model. The object-relational data model combines features of the object-oriented data model and relational data model. Chapter 9 examines it. This is in contrast with the data models mentioned earlier, where every data item of a particular type must have the same set of attributes.
The extensible markup language XML is widely used to represent semistructured data. Chapter 10 covers it. Historically, two other data models, the network data model and the hierarchical data model, preceded the relational data model. These models were tied closely to the underlying implementation, and complicated the task of modeling data. As a result they are little used now, except in old database code that is still in service in some places.
They are outlined in Appendices A and B, for interested readers. In addition, it up- dates a special set of tables called the data dictionary or data directory. A data dictionary contains metadata — that is, data about data. The schema of a ta- ble is an example of metadata.
Fixed point number, with user-specified precision of p digits, with n digits to the right of decimal point. Floating point and double-precision floating point numbers, with machine-dependent precision. Floating point number, with user-specified precision of at least n digits.
SQL names are case insensitive i. If there are c1 copies of tuple t1 in r1 and c2 copies of tuple t2 in r2, there are c1 x c2 copies of the tuple t1. Suppose multiset relations r1 A, B and r2 C are as follows: Suppose a tuple occurs m times in r and n times in s, then, it occurs: Attributes in select clause outside of aggregate functions must appear in group by list Database System Concepts, 5th Edition, Oct 5, 3. Find all loan number which appear in the loan relation with null values for amount.
Above query can be written in a much simpler manner.
The formulation above is simply to illustrate SQL features. The view name is represented by v. First, compute avg balance and find all tuples to delete 2. Next, delete all tuples found above without recomputing avg or retesting the tuples Database System Concepts, 5th Edition, Oct 5, 3. Download pdf.